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The Difference Between Supervised and Unsupervised Learning in Machine Learning
Supervised learning involves training a model on labeled data, where the algorithm learns to map input data to the correct output. It requires human intervention to provide the correct answers during the training process. On the other hand, unsupervised learning involves training a model on unlabeled data, where the algorithm tries to find patterns or relationships in the data without guidance on what the output should be.
Overall, the main difference between supervised and unsupervised learning lies in the presence of labeled data and human intervention. While supervised learning is used for tasks with clear input-output relationships, unsupervised learning is more exploratory and can uncover hidden patterns in data.
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